import pandas as pd
df=pd.read_csv('cleaned_data.csv')
df
| SNO | Category | States_UT | poverty | malnutrition | literacy | drinking water | electricity | unemployment | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.0 | State | Andhra Pradesh | 9.2 | 35.5 | 65.6 | 99.81125151 | 100 | 5.7 |
| 1 | 2.0 | State | Arunachal Pradesh | 34.67 | 16 | 79.9 | 92.63657957 | 100 | 7.7 |
| 2 | 3.0 | State | Assam | 31.98 | 29.4 | 84.9 | 74.72472158 | 100 | 7.1 |
| 3 | 4.0 | State | Bihar | 33.74 | 38.7 | 64.7 | 96.28630888 | 100 | 10.6 |
| 4 | 5.0 | State | Chhattisgarh | 39.93 | 40 | 74.1 | 99.59868138 | 99.67 | 2.6 |
| 5 | 6.0 | State | Goa | 5.09 | 20.3 | 88.9 | 100 | 100 | 9.4 |
| 6 | 7.0 | State | Gujarat | 16.63 | 34.2 | 80.7 | 100 | 100 | 3.4 |
| 7 | 8.0 | State | Haryana | 11.16 | 28.8 | 77.3 | 99.71431711 | 100 | 9.8 |
| 8 | 9.0 | State | Himachal Pradesh | 8.06 | 22.6 | 84.2 | 100 | 100 | 5.8 |
| 9 | 10.0 | State | Jharkhand | 36.96 | 42.9 | 67.3 | 99.70510686 | 100 | 5.5 |
| 10 | 11.0 | State | Karnataka | 20.91 | 32 | 75.7 | 100 | 100 | 3.9 |
| 11 | 12.0 | State | Kerala | 7.05 | 18.7 | 94.6 | 99.35308848 | 100 | 10.4 |
| 12 | 13.0 | State | Madhya Pradesh | 31.65 | 38.7 | 70.5 | 99.64891263 | 100 | 3.7 |
| 13 | 14.0 | State | Maharashtra | 17.35 | 30.9 | 80.3 | 99.70219926 | 100 | 5.4 |
| 14 | 15.0 | State | Manipur | 36.89 | 13 | 85.6 | 100 | 100 | 10.1 |
| 15 | 16.0 | State | Meghalaya | 11.87 | 30 | 91.4 | 100 | 100 | 2.8 |
| 16 | 17.0 | State | Mizoram | 20.4 | 11.3 | 98.5 | 100 | 100 | 7.3 |
| 17 | 18.0 | State | Nagaland | 18.88 | 16.3 | 93.8 | 100 | 100 | 18.5 |
| 18 | 19.0 | State | Odisha | 32.59 | 29.2 | 72.5 | 98.13119756 | 100 | 7.6 |
| 19 | 20.0 | State | Punjab | 8.26 | 19.7 | 79.4 | 92.4089014 | 100 | 8.0 |
| 20 | 21.0 | State | Rajasthan | 14.71 | 31.5 | 67.1 | 92.2759537 | 100 | 6.2 |
| 21 | 22.0 | State | Sikkim | 8.19 | 11 | 86.2 | 100 | 100 | 3.3 |
| 22 | 23.0 | State | Tamil Nadu | 11.28 | 23.5 | 80.7 | 99.39444338 | 100 | 7.2 |
| 23 | 24.0 | State | Telangana | Null | 30.8 | 67.4 | 100 | 100 | 8.8 |
| 24 | 25.0 | State | Tripura | 14.05 | 23.8 | 89.9 | 84.84415213 | 100 | 10.5 |
| 25 | 26.0 | State | Uttar Pradesh | 29.43 | 36.8 | 68.2 | 99.62768688 | 100 | 6.2 |
| 26 | 27.0 | State | Uttarakhand | 11.26 | 18.7 | 79.0 | 99.26236621 | 100 | 9.5 |
| 27 | 28.0 | State | West Bengal | 19.98 | 30.9 | 79.0 | 95.47820993 | 100 | 4.1 |
| 28 | 29.0 | Union Territory | Andaman and Nicobar Islands | 1 | Null | 88.3 | 100 | Null | 13.8 |
| 29 | 30.0 | Union Territory | Chandigarh | 21.81 | Null | 89.1 | Null | Null | 7.8 |
| 30 | 31.0 | Union Territory | Dadra and Nagar Haveli | 39.31 | Null | 77.7 | Null | Null | 1.5 |
| 31 | 32.0 | Union Territory | Daman and Diu | 9.86 | Null | 88.3 | Null | Null | 0.0 |
| 32 | 33.0 | Union Territory | Delhi | 9.91 | 28.1 | 86.4 | Null | 100 | 10.7 |
| 33 | 34.0 | Union Territory | Jammu and Kashmir | 10.35 | 13 | 76.4 | 99.889989 | 100 | 5.6 |
| 34 | 35.0 | Union Territory | Ladakh | 10.35 | 13 | 76.4 | 100 | 100 | 5.6 |
| 35 | 36.0 | Union Territory | Lakshadweep | 2.77 | Null | 95.7 | Null | Null | 32.0 |
| 36 | 37.0 | Union Territory | Puducherry | 9.69 | Null | 89.5 | 95.88550984 | 100 | 8.7 |
| 37 | NaN | NaN | India | 21.92 | 33.4 | 74.6 | 97.44222839 | 99.99 | 6.2 |
| 38 | NaN | NaN | Target | 10.96 | 1.9 | 100.0 | 100 | 100 | 3.0 |
import plotly.graph_objects as go
desc = {
"poverty": "Percentage of population living below the national poverty line",
"malnutrition": "Percentage of children under five years who are underweight",
"literacy": "Percentage of persons (15 years and above) who are literate",
"drinking water": "Percentage of rural population having improved source of drinking water",
"electricity": "Percentage of households electrified ",
"unemployment": "Unemployment rate (%) (15-59 years)",
}
values = [list(desc.keys()), list(desc.values())]
fig = go.Figure(data=[go.Table(
columnorder=[1, 2],
columnwidth=[80, 400],
header=dict(
values=[['<b>Parameter</b>'],
['<b>Description</b>']],
line_color='darkslategray',
fill_color='royalblue',
align=['left', 'center'],
font=dict(color='white', size=12),
height=40
),
cells=dict(
values=values,
line_color='darkslategray',
fill=dict(color=['paleturquoise', 'white']),
align=['left', 'center'],
font_size=12,
height=30)
)
])
fig.show()
import pandas as pd
import numpy as np
import plotly.graph_objs as go
df = pd.read_csv('cleaned_data.csv')
parameters = ["poverty",
"malnutrition",
"literacy",
"drinking water",
"electricity",
"unemployment"]
for i in range(1, 7):
print(i, parameters[i-1])
fig = go.Figure()
n = 1
while(n <= 6):
parameter = parameters[n-1]
values = df[parameter]
y = list()
for val in values:
if(val == 'Null'):
continue
else:
y.append(round(float(val), 2))
fig.add_trace(go.Box(y=y, name=parameter))
n += 1
fig.update_layout(
go.Layout(
title="Box Plot",
xaxis=dict(
title='parameters'
),
yaxis=dict(
title='value(in %)',
)
)
)
fig.show()
1 poverty 2 malnutrition 3 literacy 4 drinking water 5 electricity 6 unemployment
import pandas as pd
import numpy as np
import plotly.graph_objs as go
df = pd.read_csv('cleaned_data.csv')
parameters = ["poverty",
"malnutrition",
"literacy",
"drinking water",
"electricity",
"unemployment"]
for i in range(1, 7):
print(i, parameters[i-1])
fig = go.Figure()
n = 1
while(n <= 6):
parameter = parameters[n-1]
values = df[parameter]
y = list()
for val in values:
if(val == 'Null'):
continue
else:
y.append(round(float(val), 2))
Q1 = np.quantile(y, 0.25)
Q3 = np.quantile(y, 0.75)
IQR = Q3-Q1
y_filtered = list()
for x in y:
if(x >= Q1-1.5*IQR and x <= Q3+1.5*IQR):
y_filtered.append(x)
fig.add_trace(go.Box(y=y_filtered, name=parameter))
n += 1
fig.update_layout(
go.Layout(
title="Box Plot",
xaxis=dict(
title='parameters'
),
yaxis=dict(
title='value(in %)',
)
)
)
fig.show()
1 poverty 2 malnutrition 3 literacy 4 drinking water 5 electricity 6 unemployment
import plotly.graph_objs as go
desc = {
"poverty": "Percentage of population living below the national poverty line",
"malnutrition": "Percentage of children under five years who are underweight",
"literacy": "Percentage of persons (15 years and above) who are literate",
"drinking water": "Percentage of rural population having improved source of drinking water",
"electricity": "Percentage of households electrified ",
"unemployment": "Unemployment rate (%) (15-59 years)",
}
parameters = ["poverty",
"malnutrition",
"literacy",
"drinking water",
"electricity",
"unemployment"]
for i in range(1, 7):
print(i, parameters[i-1])
n=1
parameter = parameters[n-1]
x = list()
y = list()
for i in range(37):
val = df.loc[i, parameter]
if(val != 'Null'):
x.append(df.loc[i, 'States_UT'])
y.append(round(float(df.loc[i, parameter]), 2))
india = round(float(df.loc[37, parameter]), 2)
target = round(float(df.loc[38, parameter]), 2)
fig = go.Figure()
fig.add_trace(
go.Bar(
x=x,
y=y,
marker=dict(color=y,
colorscale='viridis', showscale=True)
)
)
fig.add_hline(y=target, line_color="red", annotation_text="Target",
annotation_position="bottom right")
fig.add_hline(y=india, line_color="blue", annotation_text="India Average",
annotation_position="bottom right")
fig.update_layout(
go.Layout(
title=desc[parameter],
xaxis=dict(
title='states/UT'
),
yaxis=dict(
title='value(in %)',
)
)
)
fig.show()
1 poverty 2 malnutrition 3 literacy 4 drinking water 5 electricity 6 unemployment
import plotly.graph_objs as go
desc = {
"poverty": "Percentage of population living below the national poverty line",
"malnutrition": "Percentage of children under five years who are underweight",
"literacy": "Percentage of persons (15 years and above) who are literate",
"drinking water": "Percentage of rural population having improved source of drinking water",
"electricity": "Percentage of households electrified ",
"unemployment": "Unemployment rate (%) (15-59 years)",
}
parameters = ["poverty",
"malnutrition",
"literacy",
"drinking water",
"electricity",
"unemployment"]
for i in range(1, 7):
print(i, parameters[i-1])
n=2
parameter = parameters[n-1]
x = list()
y = list()
for i in range(37):
val = df.loc[i, parameter]
if(val != 'Null'):
x.append(df.loc[i, 'States_UT'])
y.append(round(float(df.loc[i, parameter]), 2))
india = round(float(df.loc[37, parameter]), 2)
target = round(float(df.loc[38, parameter]), 2)
fig = go.Figure()
fig.add_trace(
go.Bar(
x=x,
y=y,
marker=dict(color=y,
colorscale='viridis', showscale=True)
)
)
fig.add_hline(y=target, line_color="red", annotation_text="Target",
annotation_position="bottom right")
fig.add_hline(y=india, line_color="blue", annotation_text="India Average",
annotation_position="bottom right")
fig.update_layout(
go.Layout(
title=desc[parameter],
xaxis=dict(
title='states/UT'
),
yaxis=dict(
title='value(in %)',
)
)
)
fig.show()
1 poverty 2 malnutrition 3 literacy 4 drinking water 5 electricity 6 unemployment
import plotly.graph_objs as go
desc = {
"poverty": "Percentage of population living below the national poverty line",
"malnutrition": "Percentage of children under five years who are underweight",
"literacy": "Percentage of persons (15 years and above) who are literate",
"drinking water": "Percentage of rural population having improved source of drinking water",
"electricity": "Percentage of households electrified ",
"unemployment": "Unemployment rate (%) (15-59 years)",
}
parameters = ["poverty",
"malnutrition",
"literacy",
"drinking water",
"electricity",
"unemployment"]
for i in range(1, 7):
print(i, parameters[i-1])
n=3
parameter = parameters[n-1]
x = list()
y = list()
for i in range(37):
val = df.loc[i, parameter]
if(val != 'Null'):
x.append(df.loc[i, 'States_UT'])
y.append(round(float(df.loc[i, parameter]), 2))
india = round(float(df.loc[37, parameter]), 2)
target = round(float(df.loc[38, parameter]), 2)
fig = go.Figure()
fig.add_trace(
go.Bar(
x=x,
y=y,
marker=dict(color=y,
colorscale='viridis', showscale=True)
)
)
fig.add_hline(y=target, line_color="red", annotation_text="Target",
annotation_position="bottom right")
fig.add_hline(y=india, line_color="blue", annotation_text="India Average",
annotation_position="bottom right")
fig.update_layout(
go.Layout(
title=desc[parameter],
xaxis=dict(
title='states/UT'
),
yaxis=dict(
title='value(in %)',
)
)
)
fig.show()
1 poverty 2 malnutrition 3 literacy 4 drinking water 5 electricity 6 unemployment
import plotly.graph_objs as go
desc = {
"poverty": "Percentage of population living below the national poverty line",
"malnutrition": "Percentage of children under five years who are underweight",
"literacy": "Percentage of persons (15 years and above) who are literate",
"drinking water": "Percentage of rural population having improved source of drinking water",
"electricity": "Percentage of households electrified ",
"unemployment": "Unemployment rate (%) (15-59 years)",
}
parameters = ["poverty",
"malnutrition",
"literacy",
"drinking water",
"electricity",
"unemployment"]
for i in range(1, 7):
print(i, parameters[i-1])
n=4
parameter = parameters[n-1]
x = list()
y = list()
for i in range(37):
val = df.loc[i, parameter]
if(val != 'Null'):
x.append(df.loc[i, 'States_UT'])
y.append(round(float(df.loc[i, parameter]), 2))
india = round(float(df.loc[37, parameter]), 2)
target = round(float(df.loc[38, parameter]), 2)
fig = go.Figure()
fig.add_trace(
go.Bar(
x=x,
y=y,
marker=dict(color=y,
colorscale='viridis', showscale=True)
)
)
fig.add_hline(y=target, line_color="red", annotation_text="Target",
annotation_position="bottom right")
fig.add_hline(y=india, line_color="blue", annotation_text="India Average",
annotation_position="bottom right")
fig.update_layout(
go.Layout(
title=desc[parameter],
xaxis=dict(
title='states/UT'
),
yaxis=dict(
title='value(in %)',
)
)
)
fig.show()
1 poverty 2 malnutrition 3 literacy 4 drinking water 5 electricity 6 unemployment
import plotly.graph_objs as go
desc = {
"poverty": "Percentage of population living below the national poverty line",
"malnutrition": "Percentage of children under five years who are underweight",
"literacy": "Percentage of persons (15 years and above) who are literate",
"drinking water": "Percentage of rural population having improved source of drinking water",
"electricity": "Percentage of households electrified ",
"unemployment": "Unemployment rate (%) (15-59 years)",
}
parameters = ["poverty",
"malnutrition",
"literacy",
"drinking water",
"electricity",
"unemployment"]
for i in range(1, 7):
print(i, parameters[i-1])
n=5
parameter = parameters[n-1]
x = list()
y = list()
for i in range(37):
val = df.loc[i, parameter]
if(val != 'Null'):
x.append(df.loc[i, 'States_UT'])
y.append(round(float(df.loc[i, parameter]), 2))
india = round(float(df.loc[37, parameter]), 2)
target = round(float(df.loc[38, parameter]), 2)
fig = go.Figure()
fig.add_trace(
go.Bar(
x=x,
y=y,
marker=dict(color=y,
colorscale='viridis', showscale=True)
)
)
fig.add_hline(y=target, line_color="red", annotation_text="Target",
annotation_position="bottom right")
fig.add_hline(y=india, line_color="blue", annotation_text="India Average",
annotation_position="bottom right")
fig.update_layout(
go.Layout(
title=desc[parameter],
xaxis=dict(
title='states/UT'
),
yaxis=dict(
title='value(in %)',
)
)
)
fig.show()
1 poverty 2 malnutrition 3 literacy 4 drinking water 5 electricity 6 unemployment
import plotly.graph_objs as go
desc = {
"poverty": "Percentage of population living below the national poverty line",
"malnutrition": "Percentage of children under five years who are underweight",
"literacy": "Percentage of persons (15 years and above) who are literate",
"drinking water": "Percentage of rural population having improved source of drinking water",
"electricity": "Percentage of households electrified ",
"unemployment": "Unemployment rate (%) (15-59 years)",
}
parameters = ["poverty",
"malnutrition",
"literacy",
"drinking water",
"electricity",
"unemployment"]
for i in range(1, 7):
print(i, parameters[i-1])
n=6
parameter = parameters[n-1]
x = list()
y = list()
for i in range(37):
val = df.loc[i, parameter]
if(val != 'Null'):
x.append(df.loc[i, 'States_UT'])
y.append(round(float(df.loc[i, parameter]), 2))
india = round(float(df.loc[37, parameter]), 2)
target = round(float(df.loc[38, parameter]), 2)
fig = go.Figure()
fig.add_trace(
go.Bar(
x=x,
y=y,
marker=dict(color=y,
colorscale='viridis', showscale=True)
)
)
fig.add_hline(y=target, line_color="red", annotation_text="Target",
annotation_position="bottom right")
fig.add_hline(y=india, line_color="blue", annotation_text="India Average",
annotation_position="bottom right")
fig.update_layout(
go.Layout(
title=desc[parameter],
xaxis=dict(
title='states/UT'
),
yaxis=dict(
title='value(in %)',
)
)
)
fig.show()
1 poverty 2 malnutrition 3 literacy 4 drinking water 5 electricity 6 unemployment
import plotly.express as px
df = pd.read_csv('cleaned_data.csv')
df.set_index('States_UT', inplace=True)
desc = {
"poverty": "Percentage of population living below the national poverty line",
"malnutrition": "Percentage of children under five years who are underweight",
"literacy": "Percentage of persons (15 years and above) who are literate",
"drinking water": "Percentage of rural population having improved source of drinking water",
"electricity": "Percentage of households electrified ",
"unemployment": "Unemployment rate (%) (15-59 years)",
}
L = ["Andhra Pradesh",
"Arunachal Pradesh",
"Assam",
"Bihar",
"Chhattisgarh",
"Goa",
"Gujarat",
"Haryana",
"Himachal Pradesh",
"Jharkhand",
"Karnataka",
"Kerala",
"Madhya Pradesh",
"Maharashtra",
"Manipur",
"Meghalaya",
"Mizoram",
"Nagaland",
"Odisha",
"Punjab",
"Rajasthan",
"Sikkim",
"Tamil Nadu",
"Telangana",
"Tripura",
"Uttar Pradesh",
"Uttarakhand",
"West Bengal",
"Andaman and Nicobar Islands",
"Chandigarh",
"Dadra and Nagar Haveli",
"Daman and Diu",
"Delhi",
"Jammu and Kashmir",
"Ladakh",
"Lakshadweep",
"Puducherry"]
for i in range(1, len(L)+1):
print(i, L[i-1])
n = 1
name = L[n-1]
x = list()
y = list()
for key in desc:
val = df.loc[name, key]
if(val == 'Null'):
continue
else:
y.append(round(float(val), 2))
x.append(key)
df = pd.DataFrame(dict(
r=y,
theta=x))
fig = px.line_polar(df, r='r', theta='theta', line_close=True, title=name)
fig.update_traces(fill='toself')
fig.show()
1 Andhra Pradesh 2 Arunachal Pradesh 3 Assam 4 Bihar 5 Chhattisgarh 6 Goa 7 Gujarat 8 Haryana 9 Himachal Pradesh 10 Jharkhand 11 Karnataka 12 Kerala 13 Madhya Pradesh 14 Maharashtra 15 Manipur 16 Meghalaya 17 Mizoram 18 Nagaland 19 Odisha 20 Punjab 21 Rajasthan 22 Sikkim 23 Tamil Nadu 24 Telangana 25 Tripura 26 Uttar Pradesh 27 Uttarakhand 28 West Bengal 29 Andaman and Nicobar Islands 30 Chandigarh 31 Dadra and Nagar Haveli 32 Daman and Diu 33 Delhi 34 Jammu and Kashmir 35 Ladakh 36 Lakshadweep 37 Puducherry
import plotly.express as px
df = pd.read_csv('cleaned_data.csv')
df.set_index('States_UT', inplace=True)
desc = {
"poverty": "Percentage of population living below the national poverty line",
"malnutrition": "Percentage of children under five years who are underweight",
"literacy": "Percentage of persons (15 years and above) who are literate",
"drinking water": "Percentage of rural population having improved source of drinking water",
"electricity": "Percentage of households electrified ",
"unemployment": "Unemployment rate (%) (15-59 years)",
}
L = ["Andhra Pradesh",
"Arunachal Pradesh",
"Assam",
"Bihar",
"Chhattisgarh",
"Goa",
"Gujarat",
"Haryana",
"Himachal Pradesh",
"Jharkhand",
"Karnataka",
"Kerala",
"Madhya Pradesh",
"Maharashtra",
"Manipur",
"Meghalaya",
"Mizoram",
"Nagaland",
"Odisha",
"Punjab",
"Rajasthan",
"Sikkim",
"Tamil Nadu",
"Telangana",
"Tripura",
"Uttar Pradesh",
"Uttarakhand",
"West Bengal",
"Andaman and Nicobar Islands",
"Chandigarh",
"Dadra and Nagar Haveli",
"Daman and Diu",
"Delhi",
"Jammu and Kashmir",
"Ladakh",
"Lakshadweep",
"Puducherry"]
for i in range(1, len(L)+1):
print(i, L[i-1])
n = 4
name = L[n-1]
x = list()
y = list()
for key in desc:
val = df.loc[name, key]
if(val == 'Null'):
continue
else:
y.append(round(float(val), 2))
x.append(key)
df = pd.DataFrame(dict(
r=y,
theta=x))
fig = px.line_polar(df, r='r', theta='theta', line_close=True, title=name)
fig.update_traces(fill='toself')
fig.show()
1 Andhra Pradesh 2 Arunachal Pradesh 3 Assam 4 Bihar 5 Chhattisgarh 6 Goa 7 Gujarat 8 Haryana 9 Himachal Pradesh 10 Jharkhand 11 Karnataka 12 Kerala 13 Madhya Pradesh 14 Maharashtra 15 Manipur 16 Meghalaya 17 Mizoram 18 Nagaland 19 Odisha 20 Punjab 21 Rajasthan 22 Sikkim 23 Tamil Nadu 24 Telangana 25 Tripura 26 Uttar Pradesh 27 Uttarakhand 28 West Bengal 29 Andaman and Nicobar Islands 30 Chandigarh 31 Dadra and Nagar Haveli 32 Daman and Diu 33 Delhi 34 Jammu and Kashmir 35 Ladakh 36 Lakshadweep 37 Puducherry
import plotly.express as px
df = pd.read_csv('cleaned_data.csv')
df.set_index('States_UT', inplace=True)
desc = {
"poverty": "Percentage of population living below the national poverty line",
"malnutrition": "Percentage of children under five years who are underweight",
"literacy": "Percentage of persons (15 years and above) who are literate",
"drinking water": "Percentage of rural population having improved source of drinking water",
"electricity": "Percentage of households electrified ",
"unemployment": "Unemployment rate (%) (15-59 years)",
}
L = ["Andhra Pradesh",
"Arunachal Pradesh",
"Assam",
"Bihar",
"Chhattisgarh",
"Goa",
"Gujarat",
"Haryana",
"Himachal Pradesh",
"Jharkhand",
"Karnataka",
"Kerala",
"Madhya Pradesh",
"Maharashtra",
"Manipur",
"Meghalaya",
"Mizoram",
"Nagaland",
"Odisha",
"Punjab",
"Rajasthan",
"Sikkim",
"Tamil Nadu",
"Telangana",
"Tripura",
"Uttar Pradesh",
"Uttarakhand",
"West Bengal",
"Andaman and Nicobar Islands",
"Chandigarh",
"Dadra and Nagar Haveli",
"Daman and Diu",
"Delhi",
"Jammu and Kashmir",
"Ladakh",
"Lakshadweep",
"Puducherry"]
for i in range(1, len(L)+1):
print(i, L[i-1])
n = 26
name = L[n-1]
x = list()
y = list()
for key in desc:
val = df.loc[name, key]
if(val == 'Null'):
continue
else:
y.append(round(float(val), 2))
x.append(key)
df = pd.DataFrame(dict(
r=y,
theta=x))
fig = px.line_polar(df, r='r', theta='theta', line_close=True, title=name)
fig.update_traces(fill='toself')
fig.show()
1 Andhra Pradesh 2 Arunachal Pradesh 3 Assam 4 Bihar 5 Chhattisgarh 6 Goa 7 Gujarat 8 Haryana 9 Himachal Pradesh 10 Jharkhand 11 Karnataka 12 Kerala 13 Madhya Pradesh 14 Maharashtra 15 Manipur 16 Meghalaya 17 Mizoram 18 Nagaland 19 Odisha 20 Punjab 21 Rajasthan 22 Sikkim 23 Tamil Nadu 24 Telangana 25 Tripura 26 Uttar Pradesh 27 Uttarakhand 28 West Bengal 29 Andaman and Nicobar Islands 30 Chandigarh 31 Dadra and Nagar Haveli 32 Daman and Diu 33 Delhi 34 Jammu and Kashmir 35 Ladakh 36 Lakshadweep 37 Puducherry
import pandas as pd
import plotly.graph_objs as go
df = pd.read_csv('cleaned_data.csv')
df.set_index('States_UT', inplace=True)
desc = {
"poverty": "Percentage of population living below the national poverty line",
"malnutrition": "Percentage of children under five years who are underweight",
"literacy": "Percentage of persons (15 years and above) who are literate",
"drinking water": "Percentage of rural population having improved source of drinking water",
"electricity": "Percentage of households electrified ",
"unemployment": "Unemployment rate (%) (15-59 years)",
}
L = ["Andhra Pradesh",
"Arunachal Pradesh",
"Assam",
"Bihar",
"Chhattisgarh",
"Goa",
"Gujarat",
"Haryana",
"Himachal Pradesh",
"Jharkhand",
"Karnataka",
"Kerala",
"Madhya Pradesh",
"Maharashtra",
"Manipur",
"Meghalaya",
"Mizoram",
"Nagaland",
"Odisha",
"Punjab",
"Rajasthan",
"Sikkim",
"Tamil Nadu",
"Telangana",
"Tripura",
"Uttar Pradesh",
"Uttarakhand",
"West Bengal",
"Andaman and Nicobar Islands",
"Chandigarh",
"Dadra and Nagar Haveli",
"Daman and Diu",
"Delhi",
"Jammu and Kashmir",
"Ladakh",
"Lakshadweep",
"Puducherry"]
for i in range(1, len(L)+1):
print(i, L[i-1])
n = 1
name = L[n-1]
x = list()
y = list()
for key in desc:
val = df.loc[name, key]
if(val == 'Null'):
continue
else:
x.append(key)
y.append(round(float(val), 2))
fig = go.Figure()
fig.add_trace(
go.Bar(
x=x,
y=y,
marker=dict(color=y,
colorscale='viridis', showscale=True)
)
)
fig.update_layout(
go.Layout(
title=name,
xaxis=dict(
title='parameters'
),
yaxis=dict(
title='value(in %)',
)
)
)
fig.show()
1 Andhra Pradesh 2 Arunachal Pradesh 3 Assam 4 Bihar 5 Chhattisgarh 6 Goa 7 Gujarat 8 Haryana 9 Himachal Pradesh 10 Jharkhand 11 Karnataka 12 Kerala 13 Madhya Pradesh 14 Maharashtra 15 Manipur 16 Meghalaya 17 Mizoram 18 Nagaland 19 Odisha 20 Punjab 21 Rajasthan 22 Sikkim 23 Tamil Nadu 24 Telangana 25 Tripura 26 Uttar Pradesh 27 Uttarakhand 28 West Bengal 29 Andaman and Nicobar Islands 30 Chandigarh 31 Dadra and Nagar Haveli 32 Daman and Diu 33 Delhi 34 Jammu and Kashmir 35 Ladakh 36 Lakshadweep 37 Puducherry
import pandas as pd
import plotly.graph_objs as go
df = pd.read_csv('cleaned_data.csv')
df.set_index('States_UT', inplace=True)
desc = {
"poverty": "Percentage of population living below the national poverty line",
"malnutrition": "Percentage of children under five years who are underweight",
"literacy": "Percentage of persons (15 years and above) who are literate",
"drinking water": "Percentage of rural population having improved source of drinking water",
"electricity": "Percentage of households electrified ",
"unemployment": "Unemployment rate (%) (15-59 years)",
}
L = ["Andhra Pradesh",
"Arunachal Pradesh",
"Assam",
"Bihar",
"Chhattisgarh",
"Goa",
"Gujarat",
"Haryana",
"Himachal Pradesh",
"Jharkhand",
"Karnataka",
"Kerala",
"Madhya Pradesh",
"Maharashtra",
"Manipur",
"Meghalaya",
"Mizoram",
"Nagaland",
"Odisha",
"Punjab",
"Rajasthan",
"Sikkim",
"Tamil Nadu",
"Telangana",
"Tripura",
"Uttar Pradesh",
"Uttarakhand",
"West Bengal",
"Andaman and Nicobar Islands",
"Chandigarh",
"Dadra and Nagar Haveli",
"Daman and Diu",
"Delhi",
"Jammu and Kashmir",
"Ladakh",
"Lakshadweep",
"Puducherry"]
for i in range(1, len(L)+1):
print(i, L[i-1])
n = 4
name = L[n-1]
x = list()
y = list()
for key in desc:
val = df.loc[name, key]
if(val == 'Null'):
continue
else:
x.append(key)
y.append(round(float(val), 2))
fig = go.Figure()
fig.add_trace(
go.Bar(
x=x,
y=y,
marker=dict(color=y,
colorscale='viridis', showscale=True)
)
)
fig.update_layout(
go.Layout(
title=name,
xaxis=dict(
title='parameters'
),
yaxis=dict(
title='value(in %)',
)
)
)
fig.show()
1 Andhra Pradesh 2 Arunachal Pradesh 3 Assam 4 Bihar 5 Chhattisgarh 6 Goa 7 Gujarat 8 Haryana 9 Himachal Pradesh 10 Jharkhand 11 Karnataka 12 Kerala 13 Madhya Pradesh 14 Maharashtra 15 Manipur 16 Meghalaya 17 Mizoram 18 Nagaland 19 Odisha 20 Punjab 21 Rajasthan 22 Sikkim 23 Tamil Nadu 24 Telangana 25 Tripura 26 Uttar Pradesh 27 Uttarakhand 28 West Bengal 29 Andaman and Nicobar Islands 30 Chandigarh 31 Dadra and Nagar Haveli 32 Daman and Diu 33 Delhi 34 Jammu and Kashmir 35 Ladakh 36 Lakshadweep 37 Puducherry
import pandas as pd
import plotly.graph_objs as go
df = pd.read_csv('cleaned_data.csv')
df.set_index('States_UT', inplace=True)
desc = {
"poverty": "Percentage of population living below the national poverty line",
"malnutrition": "Percentage of children under five years who are underweight",
"literacy": "Percentage of persons (15 years and above) who are literate",
"drinking water": "Percentage of rural population having improved source of drinking water",
"electricity": "Percentage of households electrified ",
"unemployment": "Unemployment rate (%) (15-59 years)",
}
L = ["Andhra Pradesh",
"Arunachal Pradesh",
"Assam",
"Bihar",
"Chhattisgarh",
"Goa",
"Gujarat",
"Haryana",
"Himachal Pradesh",
"Jharkhand",
"Karnataka",
"Kerala",
"Madhya Pradesh",
"Maharashtra",
"Manipur",
"Meghalaya",
"Mizoram",
"Nagaland",
"Odisha",
"Punjab",
"Rajasthan",
"Sikkim",
"Tamil Nadu",
"Telangana",
"Tripura",
"Uttar Pradesh",
"Uttarakhand",
"West Bengal",
"Andaman and Nicobar Islands",
"Chandigarh",
"Dadra and Nagar Haveli",
"Daman and Diu",
"Delhi",
"Jammu and Kashmir",
"Ladakh",
"Lakshadweep",
"Puducherry"]
for i in range(1, len(L)+1):
print(i, L[i-1])
n = 26
name = L[n-1]
x = list()
y = list()
for key in desc:
val = df.loc[name, key]
if(val == 'Null'):
continue
else:
x.append(key)
y.append(round(float(val), 2))
fig = go.Figure()
fig.add_trace(
go.Bar(
x=x,
y=y,
marker=dict(color=y,
colorscale='viridis', showscale=True)
)
)
fig.update_layout(
go.Layout(
title=name,
xaxis=dict(
title='parameters'
),
yaxis=dict(
title='value(in %)',
)
)
)
fig.show()
1 Andhra Pradesh 2 Arunachal Pradesh 3 Assam 4 Bihar 5 Chhattisgarh 6 Goa 7 Gujarat 8 Haryana 9 Himachal Pradesh 10 Jharkhand 11 Karnataka 12 Kerala 13 Madhya Pradesh 14 Maharashtra 15 Manipur 16 Meghalaya 17 Mizoram 18 Nagaland 19 Odisha 20 Punjab 21 Rajasthan 22 Sikkim 23 Tamil Nadu 24 Telangana 25 Tripura 26 Uttar Pradesh 27 Uttarakhand 28 West Bengal 29 Andaman and Nicobar Islands 30 Chandigarh 31 Dadra and Nagar Haveli 32 Daman and Diu 33 Delhi 34 Jammu and Kashmir 35 Ladakh 36 Lakshadweep 37 Puducherry